In this paper, the sparse sensor placement problem for least-squares estimation is considered, and previous novel approach of selection algorithm extended. The maximization determinant matrix which appears in pseudo-inverse operations employed as an objective function present extended approach. procedure corresponding proved to be mathematically same that previously proposed QR method when numb...

Theoretical greedy type algorithms are studied: a Weak Greedy Algorithm, a Weak Orthogonal Greedy Algorithm, and a Weak Relaxed Greedy Algorithm. These algorithms are defined by weaker assumptions than their analogs the Pure Greedy Algorithm, an Orthogonal Greedy Algorithm, and a Relaxed Greedy Algorithm. The weaker assumptions make these new algorithms more ready for practical implementation. ...

introduction several methods have been developed to aid in selecting a network of biodiversity protected areas. one of these methods is artificial intelligence which includes a number of different computer algorithms that use an objective function to find the best solution. an algorithm is a mathematical process or a set of rules used for problem solving. recently, site selection algorithms hav...

This paper present two meta heuristics, reverse greedy and future aware greedy, which are variants of the greedy algorithm. Both are based on the observation that guessing the impact of future selections is useful for the current selection. While the greedy algorithm makes the best local selection given the past, future aware greedy makes the best local selection given the past and the estimate...